Statistical machine learning is at the core of modern-day advances in artificial intelligence, but a Rochester Institute of Technology professor argues that applying it correctly requires equal parts science and art. Professor Ernest Fokou of RITs School of Mathematical Sciences emphasized the human element of statistical machine learning in his primer on the field that graced the cover of a recent edition of Notices of the American Mathematical Society.

One of the most important commodities in your life is common sense, said Fokou. Mathematics is beautiful, but mathematics is your servant. When you sit down and design a model, data can be very stubborn. We design models with assumptions of what the data will show or look like, but the data never looks exactly like what you expect. You may have a nice central tenet, but theres always something thats going to require your human intervention. Thats where the art comes in. After you run all these statistical techniques, when it comes down to drawing the final conclusion, you need your common sense.

Statistical machine learning is a field that combines mathematics, probability, statistics, computer science, cognitive neuroscience and psychology to create models that learn from data and make predictions about the world. One of its earliest applications was when the United States Postal Service used it to accurately learn and recognize handwritten letters and digits to autonomously sort letters. Today, we see it applied in a variety of settings, from facial recognition technology on smartphones to self-driving cars.

Researchers have developed many different learning machines and statistical models that can be applied to a given problem, but there is no one-size-fits-all method that works well for all situations. Fokou said using selecting the appropriate method requires mathematical and statistical rigor along with practical knowledge. His paper explains the central concepts and approaches, which he hopes will get more people involved in the field and harvesting its potential.

Statistical machine learning is the main tool behind artificial intelligence, said Fokou. Its allowing us to construct extensions of the human being so our lives, transportation, agriculture, medicine and education can all be better. Thanks to statistical machine learning, you can understand the processes by which people learn and slowly and steadily help humanity access a higher level.

This year, Fokou has been on sabbatical traveling the world exploring new frontiers in statistical machine learning. Fokous full article is available on the AMS website.

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RIT professor explores the art and science of statistical machine learning - RIT University News Services

AI is taking over almost every industry and the health industry has some of the biggest benefits to gain. Machine Learning, a discipline of AI, is showing good signs of being able to superseed human capabilities in accurately identifying mental health disorders.

CSIRO have announced the results of a study of 101 participants, that used ML to diagnose bipolar or depression. The error rate was between 20 30% and while that isnt yet better than humans and isnt ready for clinical use, it does show a promising sign for the future.

The machine-learning system detects patterns in data: a process known as training. Like autonomous drivings use of computer vision, the results get better, with the more data you can provide it. Its expected to improve as its fed more data on how people play the game.

One of the big challenges in psychiatry is misdiagnosis.

It gives us first-hand information about what is happening in the brain, which can be an alternative route of information for diagnosis.

The immediate aim is to build a tool that will help clinicians, but the long-term goal is to replace subjective diagnosis altogether. Between depression and bipolar disorder, theres a significant incidence of misdiagnosis of bipolar people as being depressed, as much as 60%. Around one third of them remain misdiagnosed for more than 10 years.

It is estimated that within 5 years, computers could be making the diagnosis, rather than humans as we improve the ability for AI to understand the complex human brain.

The study involved having users play a simple game game where you select between two boxes on screen. One box rewards you with greater frequency than the other; you have to collect the most points.

Whether you stick with orange or experiment with blue, or just randomly alternate these decisions paint a picture of how your brain works.

Often the traditional signatures of mental illness are too subtle for humans to notice, but its the kind of thing machine-learning AI thrives on. Now CSIRO researchers have developed a system they say can peer into the mind with significant accuracy, and could revolutionise mental health diagnosis.

Last year, researchers reported they hadfound a wayof analysing language from Facebook status updates to predict future diagnoses of depression.

More information at CSIRO.

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Machine learning could replace subjective diagnosis of mental health disorders - techAU

A computer image created by Nexu Science Communication together with Trinity College in Dublin, shows a model structurally representative of a betacoronavirus which is the type of virus linked to COVID-19.

Source: NEXU Science Communication | Reuters

Research has gone digital, and medical science is no exception. As the novel coronavirus continues to spread, for instance, scientists searching for a treatment have drafted IBM's Summit supercomputer, the world's most powerful high-performance computing facility, according to the Top500 list, to help find promising candidate drugs.

One way of treating an infection could be with a compound that sticks to a certain part of the virus, disarming it. With tens of thousands of processors spanning an area as large as two tennis courts, the Summit facility at Oak Ridge National Laboratory (ORNL) has more computational power than 1 million top-of-the-line laptops. Using that muscle, researchers digitally simulated how 8,000 different molecules would interact with the virus a Herculean task for your typical personal computer.

"It took us a day or two, whereas it has traditionally taken months on a normal computer," said Jeremy Smith, director of the University of Tennessee/ORNL Center for Molecular Biophysics and principal researcher in the study.

Simulations alone can't prove a treatment will work, but the project was able to identify 77 candidate molecules that other researchers can now test in trials. The fight against the novel coronavirus is just one example of how supercomputers have become an essential part of the process of discovery. The $200 million Summit and similar machines also simulate the birth of the universe, explosions from atomic weapons and a host of events too complicated or too violent to recreate in a lab.

The current generation's formidable power is just a taste of what's to come. Aurora, a $500 million Intel machine currently under installation at Argonne National Laboratory, will herald the long-awaited arrival of "exaflop" facilities capable of a billion billion calculations per second (five times more than Summit) in 2021 with others to follow. China, Japan and the European Union are all expected to switch on similar "exascale" systems in the next five years.

These new machines will enable new discoveries, but only for the select few researchers with the programming know-how required to efficiently marshal their considerable resources. What's more, technological hurdles lead some experts to believe that exascale computing might be the end of the line. For these reasons, scientists are increasingly attempting to harness artificial intelligenceto accomplish more research with less computational power.

"We as an industry have become too captive to building systems that execute the benchmark well without necessarily paying attention to how systems are used," says Dave Turek, vice president of technical computing for IBM Cognitive Systems. He likens high-performance computing record-seeking to focusing on building the world's fastest race car instead of highway-ready minivans. "The ability to inform the classic ways of doing HPC with AI becomes really the innovation wave that's coursing through HPC today."

Just getting to the verge of exascale computing has taken a decade of research and collaboration between the Department of Energy and private vendors. "It's been a journey," says Patricia Damkroger, general manager of Intel's high-performance computing division. "Ten years ago, they said it couldn't be done."

While each system has its own unique architecture, Summit, Aurora, and the upcoming Frontier supercomputer all represent variations on a theme: they harness the immense power of graphical processing units (GPUs) alongside traditional central processing units (CPUs). GPUs can carry out more simultaneous operations than a CPU can, so leaning on these workhorses has let Intel and IBM design machines that would have otherwise required untold megawatts of energy.

IBM's Summit supercomputer currently holds the record for the world's fastest supercomputer.

Source: IBM

That computational power lets Summit, which is known as a "pre-exascale" computer because it runs at 0.2 exaflops, simulate one single supernova explosion in about two months, according to Bronson Messer, the acting director of science for the Oak Ridge Leadership Computing Facility. He hopes that machines like Aurora (1 exaflop) and the upcoming Frontier supercomputer (1.5 exaflops) will get that time down to about a week. Damkroger looks forward to medical applications. Where current supercomputers can digitally model a single heart, for instance, exascale machines will be able to simulate how the heart works together with blood vessels, she predicts.

But even as exascale developers take a victory lap, they know that two challenges mean the add-more-GPUs formula is likely approaching a plateau in its scientific usefulness. First, GPUs are strong but dumbbest suited to simple operations such as arithmetic and geometric calculations that they can crowdsource among their many components. Researchers have written simulations to run on flexible CPUs for decades and shifting to GPUs often requires starting from scratch.

GPU's have thousands of cores for simultaneous computation, but each handles simple instructions.

Source: IBM

"The real issue that we're wrestling with at this point is how do we move our code over" from running on CPUs to running on GPUs, says Richard Loft, a computational scientist at the National Center for Atmospheric Research, home of Top500's 44th ranking supercomputerCheyenne, a CPU-based machine "It's labor intensive, and they're difficult to program."

Second, the more processors a machine has, the harder it is to coordinate the sharing of calculations. For the climate modeling that Loft does, machines with more processors better answer questions like "what is the chance of a once-in-a-millennium deluge," because they can run more identical simulations simultaneously and build up more robust statistics. But they don't ultimately enable the climate models themselves to get much more sophisticated.

For that, the actual processors have to get faster, a feat that bumps up against what's physically possible. Faster processors need smaller transistors, and current transistors measure about 7 nanometers. Companies might be able to shrink that size, Turek says, but only to a point. "You can't get to zero [nanometers]," he says. "You have to invoke other kinds of approaches."

If supercomputers can't get much more powerful, researchers will have to get smarter about how they use the facilities. Traditional computing is often an exercise in brute forcing a problem, and machine learning techniques may allow researchers to approach complex calculations with more finesse.

More from Tech Trends:Robotic medicine to fight the coronavirusRemote work techology that is key

Take drug design. A pharmacist considering a dozen ingredients faces countless possible recipes, varying amounts of each compound, which could take a supercomputer years to simulate. An emerging machine learning technique known as Bayesian Optimization asks, does the computer really need to check every single option? Rather than systematically sweeping the field, the method helps isolate the most promising drugs by implementing common-sense assumptions. Once it finds one reasonably effective solution, for instance, it might prioritize seeking small improvements with minor tweaks.

In trial-and-error fields like materials science and cosmetics, Turek says that this strategy can reduce the number of simulations needed by 70% to 90%. Recently, for instance, the technique has led to breakthroughs in battery design and the discovery of a new antibiotic.

Fields like climate science and particle physics use brute-force computation in a different way, by starting with simple mathematical laws of nature and calculating the behavior of complex systems. Climate models, for instance, try to predict how air currents conspire with forests, cities, and oceans to determine global temperature.

Mike Pritchard, a climatologist at the University of California, Irvine, hopes to figure out how clouds fit into this picture, but most current climate models are blind to features smaller than a few dozen miles wide. Crunching the numbers for a worldwide layer of clouds, which might be just a couple hundred feet tall, simply requires more mathematical brawn than any supercomputer can deliver.

Unless the computer understands how clouds interact better than we do, that is. Pritchard is one of many climatologists experimenting with training neural networksa machine learning technique that looks for patterns by trial and errorto mimic cloud behavior. This approach takes a lot of computing power up front to generate realistic clouds for the neural network to imitate. But once the network has learned how to produce plausible cloudlike behavior, it can replace the computationally intensive laws of nature in the global model, at least in theory. "It's a very exciting time," Pritchard says. "It could be totally revolutionary, if it's credible."

Companies are preparing their machines so researchers like Pritchard can take full advantage of the computational tools they're developing. Turek says IBM is focusing on designing AI-ready machines capable of extreme multitasking and quickly shuttling around huge quantities of information, and the Department of Energy contract for Aurora is Intel's first that specifies a benchmark for certain AI applications, according to Damkroger. Intel is also developing an open-source software toolkit called oneAPI that will make it easier for developers to create programs that run efficiently on a variety of processors, including CPUs and GPUs.As exascale and machine learning tools become increasingly available, scientists hope they'll be able to move past the computer engineering and focus on making new discoveries. "When we get to exascale that's only going to be half the story," Messer says. "What we actually accomplish at the exascale will be what matters."

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Next-gen supercomputers are fast-tracking treatments for the coronavirus in a race against time - CNBC

Artificial Intelligence (AI) is getting increasingly sophisticated day by day in its application, with enhanced efficiency and speed at a lower cost. Every single sector has been reaping benefits from AI in recent times. The Healthcare industry is no exception. Here is decoding the future trajectory of healthcare with AI.

The impact of artificial intelligence in the healthcare industry through machine learning (ML) and natural language processing (NLP) is transforming care delivery. Additionally, patients are expected to gain relatively high access to their health-related information than before through various applications such as smart wearable devices and mobile electronic medical records (EMR).

The personalized healthcare will authorize patients to take the wheel of their well-being, facilitate high-end healthcare, and promote better patient-provider communication to underprivileged areas.

For instance, IBM Watson for Health is helping healthcare organizations to apply cognitive technology to provide a vast amount of power diagnosis and health-related information.

In addition, Googles DeepMind Health is collaborating with researchers, clinicians, and patients in order to solve real-world healthcare problems. Additionally, the company has combined systems neuroscience with machine learning to develop strong general-purpose learning algorithms within neural networks to mimic the human brain.

Companies are working towards developing AI technology to solve several existing challenges, especially within the healthcare space. Strong focus on funding and starting AI healthcare programs played a significant role in Microsoft Corporations decision to launch a 5-year, US$ 40 million program known as AI for Health in January 2019.

The Microsoft program will use artificial intelligence tools to resolve some of the greatest healthcare challenges including global health crises, treatment, and disease diagnosis. Microsoft has also ensured that academia, non-profit, and research organizations have access to this technology, technical experts, and resources to leverage AI for care delivery and research.

In January 2020, these factors influenced Takeda Pharmaceuticals Company and MITs School of Engineering to join hands for three years to drive innovation and application of AI in the healthcare industry and drug development.

AI applications are only centered on three main investment areas: Diagnostics, Engagement, and Digitization. With the rapid advancement in technologies. There are exciting breakthroughs in incorporating AI in medical services.

The most interesting aspect of AI is robots. Robots are not only replacing trained medical staff but also making them more efficient in several areas. Robots help in controlling the cost while potentially providing better care and performing accurate surgery in limited space.

China and the U.S. have started investing in the development of robots to support doctors. In November 2017, a robot in China passed a medical licensing exam using only an AI brain. Also, it was the first-ever semi-automated operating robot that was used to suture blood vessels as fine as 0.03 mm.

In order to prevent coronavirus from spreading, the American doctors are relying on a robot that can measure the patients act and vitals. In addition, robots are also being used for recovery and consulting assistance and transporting units. These robots are showcasing significant potential in revolutionizing medical procedures in the future.

Precision medicine is an emerging approach to disease prevention and treatment. The precision medication approach allows researchers and doctors to predict more accurate treatment and prevention strategies.

The advent of precision medicine technology has allowed healthcare to actively track patients physiology in real-time, take multi-dimensional data, and create predictive algorithms that use collective learnings to calculate individual outcomes.

In recent years, there has been an immense focus on enabling direct-to-consumer genomics. Now, companies are aiming to create patient-centric products within digitization processes and genomics related to ordering complex testing in clinics.

In January 2020, ixLayer, a start-up based in San-Francisco, launched one of its kind precision health testing platforms to enhance the delivery of diagnostic testing and to shorten the complex relationship among physicians, precision health tests, and patients.

Personal health monitoring is a promising example of AI in healthcare. With the emergence of advanced AI and Internet of Medical Things (IoMT), demand for consumer-oriented products such as smart wearables for monitoring well-being is growing significantly.

Owing to the rapid proliferation of smart wearables and mobile apps, enterprises are introducing varied options to monitor personal health.

In October 2019, Gali Health, a health technology company, introduced its Gali AI-powered personal health assistant for people suffering from inflammatory bowel diseases (IBD). It offers health tracking and analytical tools, medically-vetted educational resources, and emotional support to the IBD community.

Similarly, start-ups are also coming forward with innovative devices integrated with state-of-the-art AI technology to contribute to the growing demand for personal health monitoring.

In recent years, AI has been used in numerous ways to support the medical imaging of all kinds. At present, the biggest use for AI is to assist in the analysis of images and perform single narrow recognition tasks.

In the United States, AI is considered highly valuable in enhancing business operations and patients care. It has the greatest impact on patient care by improving the accuracy of clinical outcomes and medical diagnosis.

Strong presence of leading market players in the country is bolstering the demand for medical imaging in hospitals and research centers.

In January 2020, Hitachi Healthcare Americas announced to start a new dedicated R&D center in North America. Medical imaging will leverage the advancements in machine learning and artificial intelligence to bring about next-gen of medical imaging technology.

With a plethora of issues driven by the growing rate of chronic disease and the aging population, the need for new innovative solutions in the healthcare industry is moving on an upswing.

Unleashing AIs complete potential in the healthcare industry is not an easy task. Both healthcare providers and AI developers together will have to tackle all the obstacles on the path towards the integration of new technologies.

Clearing all the hurdles will need a compounding of technological refinement and shifting mindsets. As AI trend become more deep-rooted, it is giving rise to highly ubiquitous discussions. Will AI replace the doctors and medical professionals, especially radiologists and physicians? The answer to this is, it will increase the efficiency of the medical professionals.

Initiatives by IBM Watson and Googles DeepMind will soon unlock the critical answers. However, AI aims to mimic the human brain in healthcare, human judgment, and intuitions that cannot be substituted.

Even though AI is augmenting in existing capabilities of the industry, it is unlikely to fully replace human intervention. AI skilled forces will swap only those who dont want to embrace technology.

Healthcare is a dynamic industry with significant opportunities. However, uncertainty, cost concerns, and complexity are making it an unnerving one.

The best opportunity for healthcare in the near future are hybrid models, where clinicians and physicians will be supported for treatment planning, diagnosis, and identifying risk factors. Also, with an increase in the number of geriatric population and the rise of health-related concerns across the globe, the overall burden of disease management has augmented.

Patients are also expecting better treatment and care. Due to growing innovations in the healthcare industry with respect to improved diagnosis and treatment, AI has gained consideration among the patients and doctors.

In order to develop better medical technology, entrepreneurs, healthcare service providers, investors, policy developers, and patients are coming together.

These factors are set to exhibit a brighter future of AI in the healthcare industry. It is extremely likely that there will be widespread use and massive advancements of AI integrated technology in the next few years. Moreover, healthcare providers are expected to invest in adequate IT infrastructure solutions and data centers to support new technological development.

Healthcare companies should continually integrate new technologies to build strong value and to keep the patients attention.

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The insights presented in the article are based on a recent research study on Global Artificial Intelligence In Healthcare Market by Future Market Insights.

Abhishek Budholiya is a tech blogger, digital marketing pro, and has contributed to numerous tech magazines. Currently, as a technology and digital branding consultant, he offers his analysis on the tech market research landscape. His forte is analysing the commercial viability of a new breakthrough, a trait you can see in his writing. When he is not ruminating about the tech world, he can be found playing table tennis or hanging out with his friends.

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Decoding the Future Trajectory of Healthcare with AI - ReadWrite